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The article discusses how data teams in the world of data analytics and business intelligence build solutions that are needed by business users and work with engineering teams who build the infrastructure for data. Analysts building these solutions must prepare their data from diverse sources, ensuring the data is sanitized for querying, which is done through data preparation tools. Data-centric AI practices can automate the cleansing of the data step, enabling you to export a cleaner version of the dataset with minimal effort. The article also explains how maintaining data quality is critical for effective data analytics and how data-centric AI is the discipline of systematically engineering the data used to build an AI system.